Machine Learning Models: What They Are and How to Build Them

Machine learning models are critical for everything from data science to marketing, finance, retail, and even more. Today, the machine learning revolution has changed not only how businesses operate, but entire industries too.

But what are machine learning models? And how are they built?

To build an understanding of machine learning models, explore what they are, how to create them, and what types of popular algorithms act as their foundation. To start learning, take a look at suggested courses and articles that can help guide you toward machine learning mastery. 

What is a machine learning model?

Machine learning models are computer programs that are used to recognize patterns in data or make predictions.

You create machine learning models by using machine learning algorithms, which undergo a training process using either labeled, unlabeled, or mixed data. Different machine learning algorithms suit different goals, such as classification or prediction modeling, so data scientists use different algorithms as the basis for different models. As you introduce data to a specific algorithm, it is modified to better manage a specific task and becomes a machine learning model.

For example, a decision tree is a common algorithm used for both classification and prediction modeling. A data scientist looking to create a machine learning model that identifies different animal species might train a decision tree algorithm with various animal images. Over time, the algorithm would become modified by the data and become increasingly better at classifying animal images. In turn, this would eventually become a machine learning model. 

Machine learning types: models vs. algorithms

Despite their differences, many people use these two terms interchangeably. Machine learning algorithms are programming procedures. They are methods created to solve a problem or complete a task. Machine learning models are the output of these procedures, containing the data and the procedural guidelines for using that data to predict new data.

How is a machine learning model built?

You can create machine learning models by training algorithms with either labeled data, unlabeled data, or a mix of both. Four primary machine learning algorithms exist:

• Supervised learning: Supervised learning occurs when an algorithm’s training uses “labeled data,” which is data tagged with a label so that an algorithm can successfully learn from it. Training labels help the eventual machine learning model know how to classify data in the manner that the researcher desires.

• Unsupervised learning: Unsupervised algorithms use unlabeled data to train an algorithm. In this process, the algorithm finds patterns in the data itself and creates its own data clusters. Unsupervised learning and pattern recognition are helpful for researchers who are looking to find patterns in data that are currently unknown to them.

• Semi-supervised learning: Semi-supervised learning uses a mix of labeled and unlabeled data to train an algorithm. In this process, the algorithm is first trained with a small amount of labeled data before being trained with a much larger amount of unlabeled data. 

• Reinforcement learning: Reinforcement learning is a machine learning technique that assigns positive and negative values to desired and undesired actions. The goal is to encourage programs to avoid the negative training examples and seek out the positive, learning how to maximize rewards through trial and error. You can use reinforcement learning to direct unsupervised machine learning.

What are parameters in machine learning models?

Before machine learning engineers train a machine learning algorithm, they must first set the hyperparameters for the algorithm, which act as external guides that inform the decision process and direct how the algorithm will learn. For instance, the number of branches on a regression tree, the learning rate, and the number of clusters in a clustering algorithm are all examples of hyperparameters. 

As the algorithm is trained and directed by the hyperparameters, parameters begin to form in response to the training data. These parameters include the weights and biases formed by the algorithm during training. The final parameters for a machine learning model are the model parameters, which ideally fit a data set without going over or under. 

While you can identify a machine learning model’s parameters, you can’t identify the hyperparameters used to create it.

Types of machine learning models

Two types of problems dominate machine learning: classification and prediction. 

You can approach these problems using models derived from algorithms designed for either classification or regression (a method used for predictive modeling). Occasionally, the same algorithm can be useful in creating either classification or regression models, depending on its training. 

Explore the following list of popular algorithms used to create classification and regression models. 

Machine learning classification models

• Logistic regression 

• Naive Bayes 

• Decision trees 

• Random forest 

• K-nearest neighbor (KNN)

• Support vector machine

Machine learning regression models 

• Linear regression

• Ridge regression 

• Decision trees

• Random forest 

• K-nearest neighbor (KNN)

• Neural network regression

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Machine learning models work with complex data sets to uncover patterns and derive insights. Whether you’re looking to become a data scientist or simply want to deepen your understanding of the field of machine learning, enrolling in an online course can help you advance your career.

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